Using Frequent Itemset Mining for Breast Cancer Detection

Objectives: This paper introduces an improvement over the use of Artificial Neural Networks (ANN) for breast cancer detection. Methods: It suggests use of frequent pattern mining for minimizing the dimensions of breast cancer database. After reduction step, the database is then input to an ANN for classification. Findings: We have shown through experimentation that the proposed model not only reduces the input database dimensions but also produces better classification results. Application: The proposed model will be highly beneficial in the field of medicine and will help in precise detection of breast cancer.